The aim of processing raw magnetic resonance data, which can originate both from magnetic resonance spectroscopy units and from magnetic resonance tomography units, is to extract the medically relevant information from the raw magnetic resonance data as well as possible. In the case of magnetic resonance tomography, the aim is accordingly a picture with high resolution of detail and low noise. That is to say, the aim is for a picture with high edge sharpness and a high signal to noise ratio (SNR).
In the case of magnetic resonance spectroscopy, the aim is to obtain from the raw data a linear spectrum with high resolution and with a high SNR. The quality in the processing of raw magnetic resonance data is thus firstly in the edge sharpness and image sharpness, i.e. in sharp pictures with high contrast. Secondly, it is in the production of a high SNR, so that the essence of the picture or of the spectrum is highlighted.
In the case of the various approaches to achieving this aim, it is usually not possible to increase the image sharpness and the SNR simultaneously. This drawback has a direct effect on the quality of the magnetic resonance tomography and spectroscopy. To improve the resolution of detail, radio-frequency “blue” noise is superimposed on a picture, for example. This produces a subjective increase in the resolution of detail. A likewise subjective improvement in the resolution is brought about in a similar manner by the application of high pass filters to the image data. This also amplifies the image noise, however.
To improve the SNR in the case of magnetic resonance pictures, known filters (such as the Hanning filter, the Fermi filter or the cosine filter in the frequency domain) are applied to the raw magnetic resonance data. The action of such filters is known from the literature, e.g.: F. J. Harris, Proc. IEEE, Vol. 66, No 1 (1978): “On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform”. It is also possible to apply postprocessing filters in the space domain, for example a mean filter, median filter or ARMA filter.
In addition, U.S. Pat. No. 4,463,375 discloses a method for medical image processing. This method specifies a reduced-noise version of a first processed image which has been obtained from a multiplicity of measurements in a multipicture system, e.g. using a computer tomography unit. The method first involves producing a second image with a high SNR from the multiplicity of measurements. The first image is then processed using a filter which reduces the noise. The second image is processed using a filter which is complementary to this filter. The weighted combination of the two images produces the reduced-noise version of the first processed image.